Adaptive Scale Compressive Tracking with Feature Integration

摘要：

　　Compressive tracking(CT)is utilized to cope with real-time tracking,which use a very sparse measurement matrix to compressive samples of targets and background,then a classifier is trained to distinguish foreground and background.However,this algorithm suffers from the drifting problem,and used the fixed size tracking box to detect,recognize,and update the samples and classifier.In order to solve these problems,we adopt a different way to extracted positive samples,and employ powerful features to exploit the advantages of feature fusion to describe target,a scale pyramid is used to realize adaptive scale tracking.Experimental results on various benchmark video sequences demonstrate the superior performance of our algorithm.

作者: Mingqi Luo Tuo Wang Bin Zhou

作者单位: The school of Electronic and Information Engineering,Xian Jiaotong University,Xian,Shanxi,China Xian Jiaotong University Suzhou Academy,Suzhou,Jiangsu,China

会议类型: 国际会议

会议名称: 2017 International Conference on Electronic Information Technology and Computer Engineering (EITCE 2017)(2017电子信息技术与计算机工程国际会议)(EITCE2017)

会议地点: 珠海

会议语种:英文

页码: 1-4

在线出版日期: 2017-09-23（万方平台首次上网日期，不代表论文的发表时间）